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Record W2472573929 · doi:10.1016/j.proeng.2016.06.264

Perceptual Thresholds for Shock-type Excitation of the Front Wheel of a Road Bicycle at the Cyclist's Hands

2016· article· en· W2472573929 on OpenAlexaff
Jean-Marc Drouet, Catherine Guastavino, Nicolas Girard

Bibliographic record

VenueProcedia Engineering · 2016
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsCentre for Interdisciplinary Research in Music Media and TechnologyMcGill UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsPerceptionShock (circulatory)VibrationEngineeringAccelerationSimulationStructural engineeringAcousticsPsychologyPhysics

Abstract

fetched live from OpenAlex

Dynamic comfort when riding a road bicycle is closely linked to road vibration transmitted to the cyclist. The perception of vibration transmitted to the cyclist while riding has recently garnered increased research attention. In this study, we present a laboratory set-up to simulate road cycling on a treadmill and use it to assess cyclist's sensitivity to shock-type excitation. We report a perceptual experiment to estimate the perceptual threshold in terms of the absorbed energy at the cyclist's hands when presented with two closely spaced impacts at the front wheel. Ten cyclists took part in a two-alternative forced choice (2-AFC) discrimination task. The results indicate that they were able to discriminate energy differences in the order of 100 mJ.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.272
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2016
Admission routes1
Has abstractyes

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